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AN ASSESSMENT OF THE INFLUENCE OF A COUNTRY’S LEVEL OF EMPOWERMENT ON ITS RESILIENCE TO MAINTAIN HEALTH AND
WELL-BEING AFTER THE IMPACT OF A NATURAL DISASTER
by Tanisha Wright-Brown
A Thesis submitted to the School of Graduate Studies in partial fulfillment of the requirements for the degree of
Master of Science in Medicine (Applied Health Services Research) Department of Community Health and Humanities
Faculty of Medicine
Memorial University of Newfoundland
May 2020
St. John’s, Newfoundland and Labrador
2 ABSTRACT
Natural disasters are happening more frequently and more intensely around the world, potentially exacerbated by climate change. There is an increasing concern to strengthen resilience in countries from the impact of these disasters. This thesis assessed the influence of empowerment on resilience using a quantitative approach, including descriptive, interrupted time series and ordinary least square regression analyses. Using data from 177 countries spanning over 16 years from 2000 to 2015, our results
demonstrated that countries with a higher level of freedom in terms of political rights or
civil liberties have greater resilience to maintain health and well-being after the impact of
a natural disaster and that these countries have a higher GDP, lower infant mortality,
longer life expectancy, and low corruption. These results provide further insights into the
factors that influence resilience and suggest that empowerment may be used as a tool for
disaster resilience and better health outcomes.
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ACKNOWLEDGEMENTS
I am excited to have been able to complete this thesis research, but not without the tremendous support, advice, and inspiration I received from numerous individuals. First, I would like to give God thanks for strengthening me and equipping me with the faith and the knowledge I needed to complete this chapter in my life. Second, I would like to extend a special thanks to my supervisor, Dr. Richard Audas, who had been more than just a mentor but an inspiration, an advisor, and one who has offered me plenty of support throughout this project. I must also make mention of the support I received from NL Support, particularly Dr. Hensley Mariathas, who, in the early stage of my research helped me to gain a better understanding of analyzing my data.
Finally, I would like to thank my family and friends who have offered their kind words of comfort and continued encouragement while I pursue my goals. Most
importantly, I would like to thank my husband, Kirk Brown. Kirk has been my tower of
strength during some of the most challenging times while I complete this project and
while completing my Master of Science Degree Program. Thanks again to those who
have helped to close a significant chapter in my life and to those who will continue to
support me in the next chapter.
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TABLE OF CONTENTS
ABSTRACT ... 2
ACKNOWLEDGEMENTS ... 3
LIST OF TABLES ... 7
LIST OF FIGURES ... 9
ABBREVIATIONS ... 10
CHAPTER 1: INTRODUCTION ... 12
1.1 Background ... 15
1.2 Statement of the Problem ... 17
1.3 Purpose of the Study ... 18
1.4 Research Questions and Hypothesis ... 19
1.5 Study Justification ... 19
1.6 Outline of the Research Study ... 20
CHAPTER 2: LITERATURE REVIEW ... 21
2.1 Introduction ... 21
2.2 Search Strategy ... 21
2.3 Natural Disasters and Economic Activity (GDP) ... 22
2.3.1 Natural Disasters ... 22
2.3.2 Economic Growth Impact of Natural Disasters ... 24
2.4 Empowerment ... 31
2.5 Resilience ... 37
2.5.1 Disaster Resilience... 39
2.6 The Effects of Natural Disasters on Health and Well-being ... 44
2.7 Summary of the Literature ... 48
CHAPTR 3: METHODOLOGY ... 50
3.1 Introduction ... 50
3.2 Research Design ... 50
3.2.1 Description of Variables and Indicators ... 54
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3.3 Population Description ... 62
3.4 Sources of Data and Collection Method ... 63
3.5 Data Processing and Methods of Analysis ... 64
3.5.1 Stage 1: Preparing the Data ... 64
3.5.2 Stage 2: Descriptive Analysis ... 67
3.5.3 Stage 3: Interrupted Time Series Analysis (ITSA) ... 67
3.5.4 Stage 4: Ordinary Least Square (OLS) Regression Analysis ... 70
CHAPTER 4: RESULTS ... 72
4.1 Descriptive Analysis ... 72
4.1.1 Natural Disasters ... 72
4.1.2 Disaster Indicators by Year and Income Group ... 74
4.1.2.1 Occurrence ... 74
4.1.3 Empowerment ... 79
4.1.4 Health ... 81
4.1.5 Resilience ... 83
4.2 Interrupted Time Series Analysis (ITSA) ... 85
4.2.2 GDP by Empowerment Levels ... 89
4.2.3 Infant Mortality by Income Levels ... 90
4.2.4 Infant Mortality by Empowerment Levels ... 91
4.2.5 Life Expectancy by Income Levels ... 92
4.2.6 Life Expectancy by Empowerment Levels ... 93
4.3 OLS Regression Analysis ... 94
4.3.1 Immediate Effect OLS Regression Results Based on GDP ITSA Coefficient Scores ... 95
4.3.2 Post Effect OLS Regression Results Based on GDP ITSA Coefficient Scores 96 4.3.3 Post Trend OLS Regression Results Based on GDP ITSA Coefficient Scores 97 4.3.4 Immediate Effect OLS Regression Results Based on Infant Mortality ITSA Coefficient Scores ... 98
4.3.5 Post Effect OLS Regression Results for Infant Mortality ... 99
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4.3.6 Post Trend OLS Regression Results for Infant Mortality ... 101
4.3.7 Immediate Effect OLS Regression Results for Life Expectancy ... 101
4.3.8 Post Effect OLS Regression Results for Life Expectancy... 103
4.3.9 Post Trend OLS Regression Results for Life Expectancy ... 103
4.4 Summary of the Findings ... 104
CHAPTER 5: DISCUSSIONS AND CONCLUSION ... 107
5.1 Overview of the Study ... 107
5.2 Discussion of the Research Findings ... 108
5.2.1 Descriptive Analysis Findings ... 108
5.2.2 Interrupted Time Series Analysis (ITSA) Findings ... 110
5.3 Implications of the Study ... 118
5.4 Conclusion ... 119
5.5 Recommendation ... 120
5.6 Limitations ... 120
5.7 Suggestions for Future Studies ... 121
References ... 123
APPENDIX ... 141
Appendix A: List of countries with average civil liberties, political rights and
corruption scores. ... 141
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LIST OF TABLES
Table 3. 1 Key Health and Resilience Indicators and Their Relevance ... 59
Table 3.2 List of Countries by Income Levels ... 61
Table 3.3 Description and List of Countries for Empowerment Indicators ... 65
Table 4.1 16-Year Average for Disaster Indicators by Income Levels ... 72
Table 4.2 Average ITSA Coefficient Scores for GDP Per Capita by Income Levels ... 85
Table 4.3 Results of Single Group ITSA with Newey West Standard Errors and One Lag ... 87
Table 4.4 Average ITSA Coefficient Scores for GDP by Empowerment Levels... 88
Table 4.5 Average ITSA Coefficient Scores for Infant Mortality by Income Levels ... 89
Table 4.6 Average ITSA Coefficient Scores for Infant Mortality by Empowerment Levels ... 90
Table 4.7 Average ITSA Coefficient Scores for Life Expectancy by Income Levels ... 91
Table 4.8 Average ITSA Coefficient Scores for Life Expectancy by Empowerment Levels ... 92
Table 4.9 OLS Regression for Immediate Effect (B
2) and Civil Liberties, Political Rights, Corruption and Income Levels ... 94
Table 4.10 OLS Regression for Post Effect (B
3) and Civil Liberties, Political Rights,
Corruption and Income Levels ... 95
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Table 4.11 OLS Regression for Post Trend and Civil Liberties, Political Rights,
Corruption and Income Levels ... 96 Table 4.12 OLS Regression for Immediate Effect (B
2) and Civil Liberties, Political
Rights, Corruption and Income Levels ... 97 Table 4.13 OLS Regression for Post Effect (B
3) and Civil Liberties, Political Rights, Corruption and Income Levels ... 98 Table 4.14 OLS Regression for Post Trend and Civil Liberties, Political Rights,
Corruption and Income Levels ... 99 Table 4.15 OLS Regression for Immediate Effect (B
2) and Civil Liberties, Political
Rights, Corruption and Income Levels ... 100 Table 4.16 OLS Regression for Post Effect (B
3) and Civil Liberties, Political Rights, Corruption and Income Levels ... 101 Table 4.17 OLS Regression for Post Trend and Civil Liberties, Political Rights,
Corruption and Income Levels ... 102
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LIST OF FIGURES
Figure 3.1 Sources of Data ... 63
Figure 3.2 Visual Depiction of an ITSA from Linden and Adams (2011) ... 68
Figure 4.1 Average Occurrence by Income Levels... 73
Figure 4.2 Average Total Deaths by Income Levels ... 74
Figure 4.3 Average Total Affected by Income Levels... 76
Figure 4.4 Average Total Damage by Income Levels ... 77
Figure 4.5 Average Civil Liberties and Political Rights Scores by Income Levels... 78
Figure 4.6 Average Corruption Scores by Income Levels ... 79
Figure 4.7 Infant Mortality and Life Expectancy by Income Levels ... 80
Figure 4.8 Infant Mortality and Life Expectancy by Empowerment Levels ... 81
Figure 4.9 GDP Per Capita by Income Levels ... 83
Figure 4.10 GDP Per Capita by Empowerment Levels ... 84
Figure 4.11 Results of Single Group ITSA with Newey West Standard Errors and One
Lag ... 88
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ABBREVIATIONS GDP – Gross Domestic Product
IPCC – Intergovernmental Panel on Climate Change WHO – World Health Organization
UNDESA – United Nations Department of Economic and Social Affairs UNDRR – United Nations Office of Disaster Risk Reductions
EM-DAT – Emergency Events Database
CRED – Centers for Disease Control and Prevention UNDP – United Nations Development Programme NASA – National Aeronautics and Space Administration ITSA – Interrupted Time Series Analysis
OLS – Ordinary Least Square
BLUE – Best Linear Unbiased Estimators Lincom – Linear combinations of estimators CPI – Corruption Perception Index
OECD – Organization for Economic Cooperation and Development DROP – Disaster Resilience of Place
CoBRA – Community Based Resilience Analysis
BRACED – Building Resilience and Adaptation of Climate Extremes and Disasters UNIGME – United Nations Inter-Agency Group for Child Mortality Estimation AMCHP – Association of Maternal and Child Health Programs
IMF – International Monetary Fund GNI – Gross National Income
UNISDR – United Nations Office of Disaster Risk Reductions
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ABBREVIATIONS
NOAA – National Oceanic and Atmospheric Administration
GMM – Gaussian Mixture Models
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CHAPTER 1: INTRODUCTION
Every year countries around the world experience various types of natural disasters, which cause severe devastations and billions of dollars in property damage, as well as significant numbers of deaths, injuries, and displaced people. Climate change has potentially exacerbated the impact of natural disasters, causing them to happen more frequently at even greater magnitude (IPCC, 2014 and Phalkey and Louis, 2016).
A natural disaster is a catastrophic act of nature that suddenly disrupts people’s lives, causing widespread sufferings, including the need for medical care and basic necessities such as food, clothing, and shelter, among other necessities of life (Assar, WHO, 1971, p.8). The economic impact on a country is often a consequence of natural disasters. The destruction of properties and human life are believed to be factors that influence a country’s economic growth (Mukherjee and Hastak, 2018). According to Noy (2009), “the amount of property damage incurred during a disaster is a negative
determinant of GDP growth performance” (p.224).
The impact of natural disasters may differ by country according to the type of disaster and vulnerabilities within the affected country. Noy and Yonson (2018) define vulnerability as “the conditions determined by physical, social, economic, and
environmental factors or processes which increase the susceptibility of an individual, a
community, assets or systems to the impacts of hazards” (p.2). It is important to note that
though vulnerabilities may intensify after the impact of a natural disaster, it may not
necessarily be the result of a natural disaster. Some countries may be more vulnerable
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because of pre-existing circumstances such as poor infrastructure, poor housing, political instability, lack of education, limited access to resources, a large population of
homelessness, etc. However, natural disasters may aggravate the situation for countries with pre-existing economic crisis and vulnerabilities, causing a more significant negative impact on health and well-being and make resilience much more difficult. Regardless, studies have shown that empowerment may have a positive effect on health outcomes and increase resiliency in these countries (Garces-Ozanne et al., 2016; Morena and Shaw, 2018; Woodhall et al., 2012).
The concept of empowerment spreads across varying disciplines and contexts and
as such, has different meanings. It may serve as a tool for gender equality, it is associated
with educational development, viewed as a level of freedom, and is conceptualized as
collective and individual approach to change (Gul, 2015; Bokova, 2017; Garces-Ozanne
et al., 2016; Matthies and Uggerhaj, 2014). The United Nations Department of Economic
and Social Affairs defines empowerment as “the process of enabling people to increase
control over their lives, to gain control over the factors and decisions that shape their
lives, to increase their resources and qualities and to build capacities to gain access,
partners, networks, a voice in order to gain control” (UNDESA, 2012, p.5). In the
context of disaster resilience, empowerment will enable countries to have greater access
to productive resources (e.g. water, land, infrastructure, credit), allow their citizens to
participate in decision-making processes that affect their lives, therefore, having the
capabilities to increase resilience (Mary Robinson Foundation, 2017).
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Like empowerment, the definition of resilience varies by disciplines such as sociology, medicine, and psychology. The general meaning for most discipline is the ability to adapt and bounce back from an event (Kafle, 2012). In terms of disaster, the United Nations Office for Disaster Risk Reduction (UNDRR) defines resilience as “the ability of a system, community or society exposed to hazards to resist, absorb,
accommodate, adapt to, transform and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its basic structures and functions through risk management” (UNDRR, 2020). The resilience of a country from the impact of a disaster depends on several factors (such as schools, transportation, healthcare, employment, and other infrastructure), and it takes years for some countries to recover. In contrast, others can recover in as little as a few months. When natural disasters strike, they cause severe devastation. So why is it that some countries can bounce back much quicker than others?
The damages and losses that one country face may vary significantly in
comparison to another based on a country's level of income, hazard probability, exposure,
sensitivity, and resilience (Hallegate, 2014). For example, some countries are affected
more by hurricanes than others, and some countries have more of its population living in
flood-prone areas. Also, poorer countries may experience more casualties due to poor
quality housing, and other countries may be able to reconstruct quicker than others
(Hallegate, 2014). In other words, there may be many factors that may influence a
country’s ability to adapt, cope and recover from natural disasters as “the impacts of
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natural disasters span across health, social, demographic and economic aspects of human life” (Phalkey and Louis, 2016, p. 2).
This chapter introduces a study aiming to ascertain whether countries with a higher level of empowerment have greater resilience to maintain health and well-being after the impact of a natural disaster. To determine this, the study will examine natural disasters, their economic effects on countries, and how countries recover from them. The chapter will first provide a background of the study, which will lead to the problem statement, then it will give the reason for conducting the research and outlines the questions that will be used to guide the study. After, it will justify the study and conclude with an outline of this thesis and a summary of the chapter.
1.1 Background
Every year, countries around the world are affected by natural disasters and suffered tremendous losses. In 2017, the Emergency Events Database (EM-DAT)
provided by the Centre for Research on the Epidemiology of Disasters (CRED), reported that 318 natural disasters occurred in 122 countries which resulted in 9503 deaths and more than 90 million people were affected with a cost that totaled US$314 billion in economic damages (EM-DAT, 2018). This database contains essential core data on the occurrence and effects of over 22,000 mass disasters in the world from 1900 to the present day. It is compiled from various sources, including UN agencies, non-
governmental organizations, insurance companies, research institutes, and press agencies
(EM-DAT, n.d.).
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The increased effect of climate change has caused an increase in the frequency and severity of natural disasters. When the temperature rises on land surfaces, it changes the hydrological cycles and heightens the intensity of drought, floods, and tropical storm cycles (Phalkey and Louis, 2016). According to the Intergovernmental Panel on Climate Change (IPCC) 2014, “each of the last three decades has been successively warmer at the earth’s surface than any preceding decades since 1850. The period from 1983 to 2012 was likely the warmest 30-year period for the last 1400 years in the Northern Hemisphere”
(IPCC, 2014, p. 2).
When natural disasters strike, they can cause a devastating effect on the country that is affected, including loss of lives, injuries, and damages to infrastructures and properties. They change the physical and mental well-being of the people affected and pose significant public health risks. Examples of these risks are food or water
contaminated with sewage, an increase in mosquito-borne and other vectors of diseases, post-traumatic stress disorder, anxiety, depression, fear, and rage (CDC, 2011).
According to the World Health Organization (WHO), “climate change affects the social
and environmental determinants of health, clean air, safe drinking water, food and secure
shelter” (WHO, 2018). Climate change is also expected to cause approximately 250,000
additional deaths per year between 2030 and 2050 as a result of heatwaves, diarrhea,
malaria, and childhood under-nutrition (WHO, 2018). “Climate change will amplify
existing risks and create new risks for natural and human systems. Risks are unevenly
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distributed and are generally greater for disadvantaged people and communities in countries at all levels” (IPCC, 2014, p.13).
An economic crisis may also be a consequence of natural disasters, causing a fall in GDP, loss of revenues, inflation, or deflation. Natural disasters are bad for the economy because of the human and physical impact, i.e. the damages to properties, the disruptions they cause to labour, financial and output markets (Noy, 2009). According to Ono (2015), “natural disasters destroy tangible assets such as buildings and equipment as well as human capital and thereby deteriorate their production capacity which may sometimes be fatal to firms and result in them being forced to close down” (p.1).
Business closures, as well as human costs, cause a significant negative effect on GDP growth rate and other economic implications. Besides, natural disasters affect vulnerable communities, affect health and well being, increase poverty and have a more significant impact on low-income countries which make resiliency more difficult and may prevent countries from recovering quickly (Stobl, 2012, Felbermayr and Groschl, 2014, Karim and Noy, 2015, Noy and Yonson 2016).
1.2 Statement of the Problem
There is a tremendous need for countries to be able to withstand and recover quickly from the impact of natural disasters. The frequency and severity of these natural disasters as a result of climate change have been wreaking havoc causing severe
devastation and affecting health and well-being in both developed and developing
countries (CRED, 2017, IPCC 2014, UNDP, 2011, Ng et al., 2015, Lowe et al. 2015).
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However, middle and low-income countries are the ones that are feeling the greatest brunt of the impact. Because these countries are usually more vulnerable, it is more difficult for them to adapt, cope and bounce back from these disasters (Hallegate, 2014, Klomp, 2016, Stobl, 2012, Noy and Yonson, 2016). Studies have shown that for countries to recover quickly from these disasters, they must reduce vulnerability and become more resilient (Bergholt, 2012, Stobl, 2012, Karim and Noy, 2014, 2015, Noy and Yonson, 2016). The resilience literature finds that countries are more resilient and experience better health outcomes when the citizens are educated and are able to make their own decisions, when women are empowered and when there is strong governance and community collaboration (Gil-Rivas and Kilmer, 2016, Arban, et al., 2016, Gul, 2016, Garces-Ozanne et al., 2016, Moreno and Shaw, 2018, Comerio, 2014).
Despite the extensive literature on disaster resilience, little is known about the influence of empowerment on the resilience to maintain health and well-being after the impact of a natural disaster.
1.3 Purpose of the Study
The purpose of this study is to determine if a country’s level of empowerment influences its resilience to maintain health and well-being after the impact of a natural disaster. The study intends to find out why some countries are more resilient than others.
It will also evaluate the health outcomes of these countries after these disasters impact
them, using life expectancy and infant mortality as proxies to measure resilience. The
study will examine over 170 countries between 2000 and 2015. The research design for
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this study will be quantitative, using both descriptive statistics and regression-based approaches. This will be done using secondary data from various sources, including the EM-DAT database, Freedom House, and the World Bank.
1.4 Research Questions and Hypothesis
The hypothesis is that more empowered countries have greater resilience to maintain health and well-being after the impact of natural disasters. As such, the following questions will serve as a guide to the research study:
•
Does a country’s level of empowerment influence its resilience to maintain health and well-being after the impact of a disaster?
•
What distinguishes countries that are highly resilient from those that are not?
1.5 Study Justification
The importance of resilience from the impact of a natural disaster is being recognized globally as a necessity, especially for low and middle-income countries.
Disasters appear to be increasing due to climate change, which causes their impacts to be more frequent and severe. Some countries are better protected from these problems, but others are much more vulnerable. Promoting greater economic and social justice is essential, and as such, there is a need to understand the factors that allow countries to respond better to these disasters.
This study will fill the knowledge gap in the existing disaster resilience literature and hopes to lead to a better understanding of how the empowerment of a country
influences its ability to recover from a natural disaster. The result of the study may enable
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low and mid-low empowered countries to assess and evaluate their coping strategies so that they may be able to plan, mitigate, anticipate, cope and recover much quicker from the impact of these disasters. This will place countries in a better position to maintain health and well-being in crisis situations. Besides, this study should serve as a reference for future researchers who are examining the impact of climate change.
1.6 Outline of the Research Study
The introductory chapter laid the foundation for the study by outlining the
background and providing a rationale for the study. Chapter two will give an overview of the existing literature by critically comparing and contrasting theories relating to natural disasters, their economic impact, empowerment, resilience, and health, and well-being.
Chapter three will describe the methodology, which will include the study design and data
sources. Chapter four will provide a detailed analysis of the findings derived from the
study. Subsequently, chapter five will conclude with a discussion of the results, strengths
of the study, its limitations, and provide recommendations and directions for future
research.
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CHAPTER 2: LITERATURE REVIEW 2.1 Introduction
This literature review will explore previous studies concerning empowerment and disaster recovery. It will first provide an overview of the type of natural disasters, then it will examine the disaster literature and discuss the economic effects of natural disasters on countries. After, it will look at the research relating to empowerment and resilience to gain an understanding of how they may relate to disaster recovery. This chapter will also analyze the literature that surrounds disasters' impact on health and well-being and will conclude with a summary of the main findings.
2.2 Search Strategy
For this literature review, a comprehensive literature search was conducted by accessing several online databases through the Memorial University online library via OneSearch. These databases include ProQuest, EBSCOhost, PubMed, Springer, Sage, Science Direct, Directory of Open Access Journals, and the World Bank eLibrary. I also used Google Scholar and the assistance of a librarian at the Memorial University library.
These searches were carried out using BOOLEAN operators. The keywords used were natural disasters, economic disaster, empowerment, resilience, health, and well-being.
Also, the literature review included information from sources such as EM-DAT, the
World Health Organization, and UNDP. The searches were restricted to mostly scholarly
and peer-reviewed articles. I eliminated information that was dated, duplicated, and was
not relevant to my search.
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2.3 Natural Disasters and Economic Activity (GDP) 2.3.1 Natural Disasters
Natural disasters kill thousands of people every year and disrupt the quality of lives of millions around the world. As a result of climate change, natural disasters have been occurring more frequently at an even higher intensity. Several studies have shown that the earth is heating up in the last three decades (WHO 2018, NASA 2017, and IPCC 2014). According to the World Health Organization, "in the last 130 years, the world has warmed by approximately 0.85 degrees Celsius with each of the last three decades being successively warmer than any preceding decade since the 1850" (WHO, 2018).
Specifically, the earth became much warmer in the last 35 years with 2016 being the warmest year on record and 2018 being the fourth warmest year since 1880 (NASA, 2017, NASA, 2019). According to NASA (2019), "the past four years are collectively the warmest years in the modern record."
The warming of the earth results in extreme weather patterns, worsening many types of natural disasters such as hurricanes, floods, heatwaves, droughts, etc. Besides the physical and economic impacts resulting from this warming, the main consequences are a risk to public health and safety which include, an increase in fatality, outbreak of diseases, pollution, poor quality drinking water and lack of access to basic resources.
For this study, natural disasters will be classified into five categories. EM-DAT
(2017), described these disasters as follows:
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Geophysical – This is a hazard originating from solid earth. Examples of these are earthquakes, volcanic eruptions, and tsunamis.
•
Meteorological – This is a hazard caused by short-lived micro to extreme
mesoscale weather and atmospheric conditions that last from minutes to days, for example, storms, hurricane, tornados, fog, and extreme temperatures.
•
Hydrological – This is a hazard caused by the occurrence of movement and distribution of surface and subsurface freshwater and saltwater, for example, floods, landslides, and wave action.
•
Climatological – This is a hazard caused by long-lived meso to macro-scale atmospheric processes, including intra-seasonal and multi-decadal climate vulnerability. Examples are drought, wildfires, and glacial lake outbursts.
•
Biological – This is a hazard caused by exposure to living organisms and their toxic substances, for example, an epidemic, insect infestation, and accident caused by animals.
Natural Disasters are happening in all categories around the world but with
greater frequency and intensity, and they cause devastation to many countries disrupting
the lives of many people. In 2017, "almost 90% of deaths due to disasters were due to
climatological, hydrological or meteorological disasters with nearly 60% of people
affected by disasters affected by floods and 85% of economic damages were due to
storms" (EM-DAT, 2017, p.1). Since 2006, hydrological disasters have been the most
frequently occurring form of natural disasters. According to the Centre for Research on
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the Epidemiology for Disasters (CRED), 51% of natural disaster occurrences were hydrological, followed by meteorological with 28.1%, then climatological and
geophysical with 11.1% and 9.1%, respectively (CRED, 2017). Also, in 2017, "the Asian continent experienced the highest disaster occurrence (43% of the total) with China being the most disaster-affected country impacted by 25 events (fifteen floods/landslides and six storms)" (EM-DAT, 2017, p.2).
In 2016, floods were the deadliest form of natural disasters in Africa, Asia, and Western Europe. Storms were the result of most natural disaster-related fatalities in North and Central America, the Caribbean, New Zealand, and Melanesia. Earthquakes caused most of the deaths in South America, and Southern Europe and extreme temperatures killed some people in East Europe (CRED, 2017, p.3). It is evident that "different types of natural disasters have different potential effects” (Stobl, 2012), and affect certain
geographical regions more frequently than others and at varying severity. Several studies conclude that developing countries are most commonly and severely affected by natural disasters (Stobl, 2012, Klomp, 2016, Comerio, 2014). According to Stobl (2012), "the last three decades have witnessed an increase in the number of occurrences and developing countries seem to be those bearing the brunt of these events and ultimately the economic consequences (p.1)."
2.3.2 Economic Growth Impact of Natural Disasters
Climate change is said to increase the frequency and intensity of disasters and, as
such, causes a significant impact on a country's economy. Natural disasters have direct
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and indirect effects. While direct impacts have to do with the loss of lives, displacement, the collapse of infrastructure and closure of businesses, etc., indirect impacts mostly relate to economic activities such as changes to production compositions, demand and supply shocks, shifting terms of trades, inflation, and deflation, etc. (Bergholt, 2012).
Bergholt (2012) believes that different types of disasters seem to have a disparate impact on the economy. He postulates that "disasters linked to climate change have a significantly larger impact than geophysical disasters" on the economy (p.62). He conducted a study on the disaster-growth-conflict relationship of 165 countries from 1980-2007. To study the short-term growth effects from natural disasters on economic growth, Bergholt employed a quantitative approach using data from EM-DAT to examine the causal relationship between different natural disasters and economic growth in the short run. He did this by estimating OLS regressions with both fixed and random effects coefficients. The results of the study proved that people affected by economic disasters are important for economic growth but those who experienced direct economic damages are of less importance (Bergholt, 2012). Also, he found that disasters resulting from the impact of climate change have a greater impact on the economy and a statistically larger impact than geophysical disasters (Bergholt, 2012). For example, between 1998 and 2017, climate-related disasters accounted for 73% of all economic losses, with the
greatest loss (46%) relating to storms, while geophysical disaster accounted for only 23%
(CRED and UNISDR, 2018).
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Likewise, Klomp (2016), also believes that different disasters have a disparate impact on the economy but has drawn a somewhat different conclusion from Bergholt. In his study, he found that the effects of meteorological and geophysical disasters caused a significant negative impact on economic growth in the short run but show a positive impact in the long term (Klomp, 2016, p.78). In like manner, "climatic and hydrological disasters have only a significant temporary adverse impact, within two years the impact disappeared, and the accumulated impact after ten years is zero" (Klomp, 2016, p.79).
While Bergholt used data from EM-DAT to conduct his analysis, Klomp used nighttime light intensity to measure the impact of large-scale natural disasters on economic development. He used this method because he believed that "for a disaster to have an empirical significant impact, it should be of a magnitude that can directly cause damage to the national production capacity, public infrastructure or affect a substantial number of people" and "many of the disaster data in EM-DAT will not have any impact on economic development" (p.71). Klomp used a dynamic data panel consisting of more than 1000 large scale disasters in more than 140 countries between 1992 and 2008 retrieved from the National Oceanic and Atmospheric Administration (NOAA). He conducted an OLS-FE estimator to determine the magnitude of the time-varying scaling factor needed to compute true light from observed light based on satellite images of nighttime light intensity in a specific country or region. According to Klomp (2016),
“true light imperfectly measured by the satellites as humidity, sunlight, moonlight and
cloud” (p.72). He assumed that observed light is related to true light and true light is
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related to GDP. By conducting his analysis, he was able to compare the growth rate of the light intensity before and after the occurrence of a large-scale natural disaster and demonstrated that “climatic and hydrological disasters cause a drop in the luminosity in developing and emerging markets, while geophysical and meteorological disasters decrease light intensity more in industrialized countries” (Klomp, 2016, p.85).
Klomp is not the only author who believes that the EM-DAT database is not an effective tool for measuring the disaster impact on economic growth. Equally, Felbermayr and Groschl (2014) cited two reasons why EM-DAT is not a useful tool. First, EM-DAT disaster intensity measures are more likely to correlate with GDP per capita because the monetary damage of a given disaster is higher in a richer economy. Second, the
possibility that insurance coverage is correlated with GDP per capita could lead to an upward bias in empirical estimates of disasters on growth per capita income, resulting from the probability of this inclusion into the database (Felbermayr and Groschl, 2014).
As such, they formulated their database and called it GeoMet. This database
represented information from geophysicists and meteorologists, which comprise the
physical strength of all-natural disasters that happened in various countries from 1970 to
2010 (Felbermayr and Groschl, 2014). They did a study to prove whether natural disasters
lower GDP. Like Klomp, they found that "natural disasters do indeed lower GDP per
capita temporarily with low- and middle-income countries experiencing the highest losses
across disaster types" (Felbermayr and Groschl, 2014, p.104).
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It is important to note that although Klomp, Felbermayr, and Groschl do not believe that EM-DAT is a good tool to measure the economic growth based on disaster impact, they do believe that it is a good source to assess the human and economic impact of natural disasters. According to Felbermayr and Groschl (2014), "EM-DAT database has proven a very useful tool for the analysis of direct human and monetary damages caused by natural disasters." Despite using nighttime light intensity to measure the effect of natural disasters on economic development, Klomp used EM-DAT information to construct several measures on the frequency and severity of natural disasters (Klomp, 2016, p.68). However, he addressed the endogeneity problem related to the economic consequences of a natural disaster by estimating a system GMM model (Klomp, 2016, p.68). He addressed this problem by adopting a decision rule that filtered the disasters included in the EM-DAT to meet several criteria and only disasters that fit those criteria would be included in the estimation. These criteria are that the number of persons killed is no less than 1000, the number of persons injured is no less than 1000, the number of affected is no less than 100000, and the amount of damages is no less than US$1 billion.
Other studies implied that natural disasters lower economic growth and create
vulnerable communities, increase poverty and inequalities (Bergholt, 2012, Stobl, 2012,
Karim and Noy, 2015, Noy and Yonson, 2016). Vulnerable populations are usually faced
with greater risks. They may suffer negative impacts from these disasters, and when there
is an economic crisis, it exacerbates the situation causing economic vulnerabilities. Karim
and Noy (2015) argued that the reason for this is that direct damages are not evenly
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distributed and that there are differences in the costs associated with natural disasters across different countries as a result of income. In other words, this happens most dramatically in countries where those resources suffer less because they may be economically more stable, have access to better supports and have a stronger voice to advocate for their own needs. According to Karim and Noy (2015), "countries with higher permanent income and wealth will be able to devote more resources to prevention and mitigation and that poorer households are more vulnerable and will bear direct damages" (p.13, 4). In an earlier study, Karim and Noy (2014) did a meta-regression analysis of the existing literature on the impacts of disasters on households focusing on the poor and poverty measures. They “extracted 161 observations from 38 studies of direct and indirect impact on poverty and welfare indicators impacted through different types of sudden and slow onset naturally occurring events” (Karim and Noy, 2014, p.6).
The measures of poverty and welfare outcomes were accumulated and grouped in several categories which comprise income, consumption, poverty, wealth, health, education and labour (Karim and Noy, 2014). They found that natural disasters have an adverse effect on families in general, but the effect is more significant on people with lower incomes and wealth.
More recently, Noy and Yonson (2016), explored economic vulnerability and
resilience and their relation to natural hazards. Noy and Yonson (2016) used econometric
methods to identify the underlying factors influencing vulnerability and resilience. They
considered vulnerability to be a pre-disaster concern that is linked to prevention,
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preparedness and mitigation while resilience is viewed as a post-disaster issue linked to response, rehabilitation, reconstruction and recovery (Noy and Yonson, 2016). The results of the study indicated, "that development influences vulnerability to disasters but there is a difference in the findings as to the direction of the relationship between the level of economic development and disasters as well as the extent to which the level of
development influences vulnerability between developed and developing countries and regions" (Noy and Yonson, 2016, p. 17).
In response, Klomp posits that "developing countries are more affected by the frequency effect of disasters caused by hydrological disasters while economic
development in industrialized countries reacts more strongly to the scale effect of
geological and meteorological disasters” (Klomp, 2016, p. 81). Both authors summarized that natural disasters have a greater impact on low-income countries. Noy and Yonson demonstrated that countries with a higher level of development are more resilient to natural disasters and countries with a lower level of development are more vulnerable and less resilient (Noy and Yonson, 2016, p.20,24) while Klomp, in his study, shows that countries that are more financially developed experienced a less severe impact from natural disasters (Klomp, 2016).
According to Cred and UNISDR (2018), “people in the poorest countries were on
average six times more likely than people in rich nations to be injured, lose their homes,
be displaced or evacuated, or require immediate medical assistance, food or shelter and
suffer the consequences of damage to critical infrastructure including the loss of public
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utilities, damaged schools, health facilities and places of employment (p.21). As a result, these countries become even more vulnerable and less resilient to the impact of natural disasters and because more developed countries better infrastructure and the resources, they are better able to combat the impact of these disasters, making them more resilient.
These authors found that natural disasters do have a severe impact on the affected countries. While everyone is affected, there is evidence to prove that wealthier countries are able to prevent and mitigate the economic impact of disasters because they have the resources to do so. Vulnerable and more impoverished countries find it a lot more challenging to cope.
2.4 Empowerment
The definition of empowerment may be viewed from many different concepts.
"How the concept is defined depends on the life situation of those who define it. Today, the term empowerment is often used to refer to a wide range of very different processes and practices and is used in many academic disciplines" (Matthies and Uggerhaj, 2014, p.72, p.64).
According to Matthies and Uggerhaj (2014), "Empowerment should be seen as a
process and not an outcome. It is a never-ending process because people's life and wishes
are constantly changing" (p.72). They also stated that "promoting empowerment means
believing that people are capable of making their own choices and decisions and that
human beings possess the strength and potential to resolve their own life situations and
are willing to contribute to society" (p.63).
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From a legal standpoint, Cisse et al. (2013) believed that empowerment might be achieved by "using the law as a tool to improve one's life" (p.34). The authors further stated that "legal empowerment advances the rule of law in the sense that empowered people will be in a position to demand good governance, and it transcends the rule of law by lifting the focus from governance to more general poverty alleviation” (p.34).
"Empowerment is also viewed as an approach to enable people who lack the power to become more powerful and gain some degree of control over their lives and health"
(Woodhall et al, 2012, p.1). According to Woodhall et al. (2012), "empowerment concerns combating oppression and injustice and is a process by which communities work together to increase the control they have over events that influence their lives and health" (p.1). Specifically, "empowerment is a matter of freeing this oppressed will through participatory resilience programming enabling subjects to make their own adaptation decisions and then realize these goals (Grove, 2014, p.244).
Garces-Ozanne et al. (2016) defined empowerment as individual and collective.
They described individual empowerment as "having the autonomy to make meaningful
decisions about their lives" and collective empowerment as "a devolution of decision
making to communities or groups to allow them to take charge of their own fortunes." In
other words, "recovery programs that engaged citizens in decisions about the future, have
the advantage of empowering these individuals, turning passive into active, turning lack
of control into control and promoting community engagement" (Comerio, 2014, p.64).
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Likewise, Matthies and Uggerhaj (2014) also viewed empowerment as both individual and collective. In their book, Petra Videmsek, along with five experts-by- experience in the field of mental health, performed participatory user research. This research was done from 2007 to 2008 to find out how do these experts understand and define the concept of empowerment. It was done by controlling the empowerment process, using a basic questionnaire that participants were required to complete before, after six months, and at the end of the research (p.66). Videmsek found that "on an individual level, participants gain more self-esteem, and they recognized themselves as experts on their particular condition” (p.70). Through participation, "they gain a sense of mastery over one's life which appears to be central in attaining a high level of functioning and good outcomes from the illness" (p.70). However, Woodhall et al. (2012) noted that
"individual empowerment does not consider or challenge the social determinants of people's health and does not constitute full empowerment in the sense of transforming the relations of power. Individual empowerment alone has a limited impact on addressing health inequalities and may be illusory in that it does not lead to an increase in actual power or resources" (p.2). In other words, power is only something that can be exercised and not a thing in and of itself. The possibilities for exercising power and making change reside within groups, communities or countries because social conditions enable them to participate in the process.
As Cisse et al. (2013) believed that poverty alleviation promotes empowerment,
Garces-Ozanne et al. (2016), also summarized that poverty reduction, increased access to
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education, and readily available and affordable information and communication
technology may empower individuals. The study measured empowerment by measuring political rights and civil liberties at the national level, which revealed that “wealth, education and empowerment in terms of political rights and civil liberties promote better health outcomes” (Garces- Ozanne, et al., 2016).
While Garces-Ozanne et al. (2016) used political rights and civil liberties to measure empowerment, Gul (2016) measured women’s empowerment in seven dimensions. That is economic empowerment, freedom of movement, political
empowerment, community-level empowerment, asset ownership, marriage decisions, and leadership. In 2015, Gul conducted a pilot study in rural Khyber-Pakhtunkhwa (KP) province of Pakistan, which was hit by a flood in 2010 by collecting information regarding households' financial and physical capital for a year before the storm, a year after and also in 2015 (p.5). She used linear regression models to determine if there was a link between women's empowerment levels in a household and resilience. Using a
dynamic approach to estimate resilience, Gul (2016), collected households’ financial and physical capital data for three years, a year before the disaster, a year immediately after the disaster, and five years after that year, and measured resilience by the change in capital over time. The results revealed, "that high resilience contributes to empowering women and empowered women contributes towards increasing households' resilience"
(Gul, 2016, p.39). In other words, the more resilient the household, the greater the level of
women empowerment.
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Conversely, Moreno and Shaw (2018) proposed that "resilience can be the pathway to produce long term changes in gender relation and empower women in the context of disaster" (p.217). Moreno and Shaw studied the response and recovery phases of the 2010 earthquake and tsunami which hit El Morro, one of the poorest communities in Chile, over 7 years. They did this to find out the conditions under which disasters trigger changes in gender relations and if resilience contributes to reducing women's vulnerability in the long term (p.209). Their study was done using an inductive approach, which included a variety of data collection methods generated from 54 semi-structured interviews with residents, municipal officials, NGO practitioners, and relief workers (pp.210, 211). The results showed "that disasters can trigger long-term changes in gender relations, even in highly patriarchal context and that the internal aspects of leadership and women's organizations suggest that changes can be stimulated "from the inside out" by promoting women's inner strengths, mutual learning, and collaboration" (pp.220,221).
Additionally, "investing in building women's resilience both internally and
externally can increase their adaptive capacity to climate change and disaster which can
be encouraged by gender-sensitive programs at the national and local levels that address
gender relations from the holistic and multi-stakeholder approach and improve gender
inequality and women's empowerment" (p.221). Actually, in El Morro, over the seven
years, women have become more empowered in the community and have contributed
economically, socially and politically to the community’s development by developing
management skills and becoming leaders which eventually contributed to reducing the
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unequal gender relations in the community and breaking historical patriarchal regimes.
(Morena and Shaw, 2018).
Morena and Shaw (2018) referred to these women as being “active agents of change” and no longer “passive victims” (p.216) because the conditions in which they could exercise some power over their lives were changed, creating opportunities to become more empowered. After the 2010 earthquake, women’s economic roles were changed with them contributing to an upsurge in women’s activism. For example, an organization in El Morro called the Fisherman’s Union was led by men since 1941, but after the disaster, it was led by a woman and of all the male-only organizations in the community, only remains the same (Morena and Shaw, 2018).
According to Gul (2016), "empowerment serves as an important tool in addressing gender inequality" p.13). Her models further suggested that "the ratio of literate women and education of the household head are contributing factors in improving women empowerment score" (p.6). Like Garces-Ozanne et al. (2016) and Gul (2015), Bokova (2017) believed that education plays a vital role in making an individual, community, or country more empowered when he stated that "education is not only a right, it is also a force of empowerment. It gives boys, girls, women, and men the tools to make the most of change and withstand its pressure" (Bokova, 2017, p.4). According to Bokova (2017),
"the soft power of education, culture, the sciences, communication, and information is a
lifeline in times of trials and are what determine the capacity to resist, to anticipate and to
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adapt to a changing or dangerous environment when institutions and infrastructures are jeopardized or weakened in conflict or disaster situations" (p.2).
Similarly, Noy and Yonson (2016) stated that "households with high levels of education are more resilient to the adverse effects of floods and droughts" (p.24). This was demonstrated in a previous study conducted by Noy (2009). He did a two-fold study on 109 countries from 1970 to 2003 using a panel data set to quantify the short-run impact of disasters on the macroeconomy and to examine the determinants of these impacts. The second inquiry results revealed that "countries with higher income per capita, greater trade openness and literacy rate, higher levels of public spending and better institutions are able to withstand the initial impact of disasters and are also able to prevent spillovers" (Noy and Yonson, 2016, p.20). This, therefore, means that more educated people with greater freedom are better able to make more informed decisions regarding their health and are more resilient from the adverse effect of natural disasters.
2.5 Resilience
There is varied literature on the theory of resilience, and this theory has been used in different subject areas such as archaeology, sociology, medicine, and psychology, with each having a different perspective, and each may have a different definition based on the subject area. The American Psychological Association provides a general
definition of resilience: "the process of adapting well in the face of adversity, trauma,
tragedy, threats or significant sources of stress such as family and relationship problems,
serious health problems or workplace and financial stressors (APA, 2014)." The foci of
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resilience are said to be "on the recovery and return time following a disturbance and on how much a system can be disturbed and still persist without changing function" (Kafle, 2012, p.317).
Ledesma (2014) viewed resilience as the ability to bounce back from adversity, frustration, and misfortune, while Zimmerman (2013) saw it as a protective and
compensatory model. He described the compensatory model as a "protective factor that neutralizes risk in a counteractive fashion" and the protective factor as "promotive assets or resources that modify the relationship between a risk and the promotive factor and outcomes" (pp.2, 3).
According to the Committee on Increasing National Resilience to Hazards, Engineering and Public Policy Committee on Science and the National Academies and Global Affairs staff (2012), "resilience is not a task that can be marked as "completed", no perfect end state or end condition of resilience exist." The process of building resilience requires continuous assessment, planning, and refinement by the community and all levels of government. In fact, "building resilience means building strong
communities that contain adequate essential public and private services including schools, transportation, healthcare, utilities, roads and bridges, public safety and businesses"
(pp.18,19). As such, community resilience may be defined as "an ideal condition where
the community has the capacity to anticipate, prepare for, respond to and recover from
quickly from the impacts of disasters" (Kafle, 2012, p.317). Particularly, "a resilient
community is able to respond to change or stress in a positive way and is able to maintain
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its core function as a community despite these stresses" (Kafle 2012, p.318). While there are many views of resilience, this literature review will focus on the theory of disaster resilience.
2.5.1 Disaster Resilience
The study of disaster resilience has occurred since the late 1970s and was seen as a positive reflection of vulnerability. However, disaster resilience has a lot to learn from climate change adaptation and is defined as "the ability to anticipate, adapt, absorb and recover" (Matyas and Pelling, 2015, p.11). Fan (2015) regarded resilience as "the key to developing sustainable methods of "living with risk" (p.27), while Arbon et al. (2016) viewed resilience as being collaborative and coordinated. They believed that for countries to be resilient, "a coordinated and collaborated effort is required to enhance the capacity of countries to withstand and recover from emergencies and disasters" (p.1). This coordinated and collaborated effort must be established at multiple levels across various disciplines and sectors to influence the economic, social-cultural, and political forces that shape the community (Gil-Rivas and Kilmer, 2016, p.1322). "A community was
considered to be resilient when members of the population were connected to one another
and worked together so that they were able to function and sustain, critical systems, even
under stress, adapt to changes in the physical, social and economic environment; be self-
reliant if external resources were limited or cut off; and learned from experience to
improve itself over time" (Arbon et al, 2016, p.3).
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UNDP (2014) defined building resilience as a "transformation process of strengthening the capacity of men, women, communities, institutions, and countries to anticipate, prevent, recover from and transform in the aftermath of shocks, stresses, and change”(p.4). Critically, Matyas and Pelling (2015) argued that it is not possible to bounce back to the same position once learned from an experience. This is because the individuals and organizations within the structures have been changed and that for resilience to happen, reflexivity in decision making (i.e., using personal feelings or instincts to influence the decision-making process), social learning, and self-organization must be primary components of resilience.
Conversely, Cutter et al. (2010) believed that "resilience is a set of capacities that can be fostered through interventions and policies which in turn help build and enhance a community's ability to respond and recover from disasters” (p2). In their study, they used a theoretical framework and the disaster resilience of place (DROP) model to measure the recovery progress after a disaster impact and to analyze the present conditions influencing resilience within communities. They examined 736 counties within the US Federal
Emergency Management Agency Region IV by developing baseline resilience indicators for communities (BRIC). These indicators are classified in the following five
components:
•
Social Resilience – This is the differential social capacity within and between
communities. Social resilience is evident in communities that exhibit higher levels
of education equality, fewer elderly, disabled residents, non-native English-
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speaking residents, a high percentage of inhabitants with vehicle access, telephone access, and health insurance may also demonstrate higher levels of resilience (Cutter et al., 2010).
•
Economic Resilience – This "measures the economic viability of communities, including housing capital, equitable incomes, employment, business size, and physician access" (p.8).
•
Institutional Resilience – This "contains characteristics related to mitigation, planning and prior disaster experience" (p.8).
•
Infrastructural Resilience – This refers to the evaluation of “community response and recovery capacity, for example, sheltering, vacant rental housing units, and healthcare facilitator" (p.9).
•
